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In this text we review a few structural properties of matrix models that should at least partly generalize to random tensor models. We review some aspects of the loop equations for matrix models and their algebraic counterpart for tensor…

Mathematical Physics · Physics 2016-03-08 Stephane Dartois

Through the Monte Carlo simulation of the three-dimensional, three-state Potts model, which is a paradigm of finite-temperature pure gauge QCD, we study the fluctuations of generalized susceptibilities near the temperatures of external…

High Energy Physics - Lattice · Physics 2015-06-22 Xue Pan , Mingmei Xu , Yuanfang Wu

Topologically ordered systems are characterized by topological invariants that are often calculated from the momentum space integration of a certain function that represents the curvature of the many-body state. The curvature function may…

Mesoscale and Nanoscale Physics · Physics 2016-01-21 Wei Chen

Perturbing the standard Gross-Neveu model for $N^3$ fermions by quartic interactions with the appropriate tensorial contraction patterns, we reduce the original $U(N^3)$ symmetry to either $U(N)\times U(N^2)$ or $U(N)\times U(N)\times…

High Energy Physics - Theory · Physics 2019-02-13 Dario Benedetti , Nicolas Delporte

We examine a model of non-self-avoiding, fluctuating surfaces as a candidate continuum string theory of surfaces in three dimensions. This model describes Dynamically Triangulated Random Surfaces embedded in three dimensions with an…

High Energy Physics - Theory · Physics 2007-05-23 Mark Bowick , Paul Coddington , Leping Han , Geoff Harris , Enzo Marinari

This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation---which…

Machine Learning · Computer Science 2014-11-17 Anima Anandkumar , Rong Ge , Daniel Hsu , Sham M. Kakade , Matus Telgarsky

The identification of nonlinear dynamics from observations is essential for the alignment of the theoretical ideas and experimental data. The last, in turn, is often corrupted by the side effects and noise of different natures, so…

Machine Learning · Computer Science 2020-06-08 Anna Shalova , Ivan Oseledets

We consider the one-dimensional extended Hubbard model in the presence of an explicit dimerization $\delta$. For a sufficiently strong nearest neighbour repulsion we establish the existence of a quantum phase transition between a mixed…

Strongly Correlated Electrons · Physics 2015-06-25 H. Benthien , F. H. L. Essler , A. Grage

This thesis focuses on renormalization of quantum field theories. Its first part considers three tensor models in three dimensions, a Fermionic quartic with tensors of rank-3 and two Bosonic sextic, of ranks 3 and 5. We rely upon the…

High Energy Physics - Theory · Physics 2020-10-16 Nicolas Delporte

The definition of a double-scaling limit represents an important goal in the development of tensor models. We take the first steps towards this goal by extracting and analysing the next-to-leading order contributions, in the 1/N expansion,…

High Energy Physics - Theory · Physics 2015-06-15 Wojciech Kaminski , Daniele Oriti , James P. Ryan

Tensor time series data appears naturally in a lot of fields, including finance and economics. As a major dimension reduction tool, similar to its factor model counterpart, the idiosyncratic components of a tensor time series factor model…

Methodology · Statistics 2022-08-09 Weilin Chen , Clifford Lam

We study order-disorder transitions in three-dimensional \textsl{multi-colored} loop models using Monte Carlo simulations. We show that the nature of the transition is intimately related to the nature of the loops. The symmetric loops…

Statistical Mechanics · Physics 2025-02-14 Soumya Kanti Ganguly , Sumanta Mukherjee , Chandan Dasgupta

Marginal Structural Models (MSM) are the most popular models for causal inference from time-series observational data. However, they have two main drawbacks: (a) they do not capture subject heterogeneity, and (b) they only consider fixed…

Machine Learning · Computer Science 2020-10-19 Debmalya Mandal , David Parkes

We define a new large $N$ limit for general $\text{O}(N)^{R}$ or $\text{U}(N)^{R}$ invariant tensor models, based on an enhanced large $N$ scaling of the coupling constants. The resulting large $N$ expansion is organized in terms of a…

High Energy Physics - Theory · Physics 2019-04-23 Frank Ferrari , Vincent Rivasseau , Guillaume Valette

We analyze in full mathematical rigor the most general quartically perturbed invariant probability measure for a random tensor. Using a version of the Loop Vertex Expansion (which we call the mixed expansion) we show that the cumulants…

Mathematical Physics · Physics 2015-06-15 Razvan Gurau

We introduce and briefly analyze the rainbow tensor model where all planar diagrams are melonic. This leads to considerable simplification of the large N limit as compared to that of the matrix model: in particular, what are dressed in this…

High Energy Physics - Theory · Physics 2017-05-29 H. Itoyama , A. Mironov , A. Morozov

Tensor models are used nowadays for implementing a fundamental theory of quantum gravity. We define here a polynomial $\mathcal T$ encoding the supplementary topological information. This polynomial is a natural generalization of the…

Combinatorics · Mathematics 2011-08-23 Adrian Tanasa

We obtain full-color three-loop three-point form factors of the stress-tensor supermultiplet and also of a length-3 half-BPS operator in N=4 SYM based on the color-kinematics duality and on-shell unitarity. The integrand results are…

High Energy Physics - Theory · Physics 2021-11-08 Guanda Lin , Gang Yang , Siyuan Zhang

Transformers process images and videos by flattening space and time into long token sequences. While attention and KV caching preserve past features, their memory grows with sequence length and they lack an explicit, persistent spatial…

Computer Vision and Pattern Recognition · Computer Science 2026-05-28 Kabir Swain , Sijie Han , Daniel Karl I. Weidele , Mauro Martino , Antonio Torralba

The exponential family of random graphs represents an important and challenging class of network models. Despite their flexibility, conventionally used exponential random graphs have one shortcoming. They cannot directly model weighted…

Probability · Mathematics 2016-07-15 Mei Yin